Imaging devices are of common use for controlling production on automated production lines. For example, on bottling lines, strobe light (using LED lighting unit controlled by a laser trigger device, for example) illuminates bottles transported on a conveyor, and digital cameras take digital images of the so illuminated bottles; image processing means then automatically detect an outline of the bottles on these digital images and identify different types of bottles (from their shape and/or dimensions) present on the conveyor. Such identification is used, for example, for correctly labeling the bottles according to their type (shape or content, etc.).
Image processing means can also detect colors printed on labels comprised on a packaging of the item, or on the item itself (for example, on the bottles produced on a bottling line), or directly printed onto the item, e.g. a container (for example, on cans produced on a canning line), so as to allow packaging inspection and/or pattern identification (for example, identifying brand pattern through its matching with a template image). For example, on canning lines, brand patterns can furthermore be printed either directly onto the cans or onto sleeves which fit tightly around the cans.
There are many known techniques relating to image retrieval and processing (in the field of content-based image retrieval) which can be used for identifying or inspecting an item from its digital image. However, these techniques either lack precision or involve time consuming calculations, and are thus not fully adapted for automated production line control, particularly on high speed production lines.
For example, the classical thresholding technique in the RGB color space (“Red Green Blue”) lacks precision as it does not allow separating color information from intensity information.
As another example, US patent application US 2004/0218837 A1 discloses an image processing technique wherein a digital image of an item is first segmented into blocks, and for each block a color histogram and a brightness (luminance) histogram are established. An average brightness is further determined from the brightness histogram, for each block, as a brightness feature information, and a representative color (for example, an average color) is determined from the color histogram, for each block, as a color feature information. Then, a comparison between color feature information of the digital image and color feature information of a target comparison image is performed (on a block-by-block basis) so as to decide whether the digital image is similar to the target image. If the comparison of colors is not conclusive, a further comparison between color feature information of the digital image and color feature information of the target comparison image is performed (also on a block-by-block basis).
However, such image processing technique has the inconvenience that the determination of the representative color and the average brightness for each block, both, involve a heavy calculation load (at least for determining the said two histograms) and the use of only one or both of these “average” parameters for estimating the similarity may not be precise enough in the context of a high speed production line (for example, for identifying a packaging or a brand on a packaging).
Imaging systems have been recently developed in order to identify items based on color features in a HSL (“Hue Saturation Luminance”) color space, extracted from digital images of these objects in the context of assembly and packaging inspection applications.
For example, US patent application US 2004/0228526 A9 discloses a system for color characterization using “fuzzy pixel classification” with application in color matching and color match location. This system uses color feature vectors for measuring the similarity between color images, based on a distance between color feature vectors, and for locating regions of a target image of which color information matches that of a template image. A color feature vector is herein constituted by the percentage of pixels assigned to each bin (i.e. color category) of a HSL color histogram of the target image based on HSL values of the respective pixels (i.e. each bin corresponding to values of hue, saturation and luminance), a pixel having a weight fractionally distributed across a plurality of bins, according to a “fuzzy membership function” of a “fuzzy pixel classification”.
However, such “fuzzy pixel classification” within a HSL histogram (with bins in a three-dimensional HSL space) and the subsequent similarity determination have the inconvenience to be highly demanding in computing resources. Thus, this image processing technique is not well adapted to real-time item identification on high speed production lines, especially if every single item passing on the line needs to be identified.